license: "cc-by-nc-4.0" tags:

• 视觉
• 视频分类

TimeSformer（基础尺寸模型，基于Kinetics-600微调）

TimeSformer模型在Kinetics-600数据集上进行了预训练。该模型由Tong等人在论文TimeSformer: Is Space-Time Attention All You Need for Video Understanding?中提出，并首次发布于此代码库。

免责声明：发布TimeSformer的团队未为此模型编写模型卡，因此本模型卡由fcakyon撰写。

预期用途与限制

您可以使用原始模型将视频分类为600种可能的Kinetics-600标签之一。

使用方法

以下是使用此模型对视频进行分类的示例：

from transformers import AutoImageProcessor, TimesformerForVideoClassification
import numpy as np
import torch

video = list(np.random.randn(8, 3, 224, 224))

processor = AutoImageProcessor.from_pretrained("fcakyon/timesformer-base-finetuned-k600")
model = TimesformerForVideoClassification.from_pretrained("fcakyon/timesformer-base-finetuned-k600")

inputs = processor(images=video, return_tensors="pt")

with torch.no_grad():
  outputs = model(**inputs)
  logits = outputs.logits

predicted_class_idx = logits.argmax(-1).item()
print("预测类别:", model.config.id2label[predicted_class_idx])

更多代码示例，请参阅文档。

BibTeX条目及引用信息

@inproceedings{bertasius2021space,
  title={Is Space-Time Attention All You Need for Video Understanding?},
  author={Bertasius, Gedas and Wang, Heng and Torresani, Lorenzo},
  booktitle={International Conference on Machine Learning},
  pages={813--824},
  year={2021},
  organization={PMLR}
}